What is an AI-native coding interview?

An AI-native coding interview is one where using an AI coding agent is the default way a candidate is expected to solve the problem, not a tool they happen to have open. Instead of writing a function from memory, the candidate works inside an editor with an agent — Claude Code, Codex, Cursor, or similar — on a task that's deliberately ambiguous or spans an existing codebase. The interview scores how they direct, verify, and override the agent, not whether they can produce working code unaided.

What is an agentic coding assessment?

"Agentic coding assessment" is the same idea under a different name, and the term companies running these formats tend to use for the product itself. CodeSignal, for example, describes its version as letting candidates "complete coding interviews using modern AI coding agents (such as Claude Code, Codex, Cursor and similar tools)" and giving hiring teams a transcript of the interaction, so the evaluation covers the process, not just the submitted diff (CodeSignal, Agentic Interviewing). Whether you call it agentic, AI-enabled, or AI-native, the shared feature is the same: the agent is part of the task, not a thing the candidate sneaks in.

How is this different from a traditional coding interview?

A traditional coding interview tests independent recall and implementation — can the candidate reverse a linked list or implement a sort from scratch, under time pressure, with no assistance. An AI-native interview assumes the agent is there from the start and tests a different skill entirely: can the candidate turn an ambiguous ask into a prompt the agent can act on, read its output critically, and catch the moment it's confidently wrong. The problems themselves usually change shape too — fewer isolated functions, more multi-file tasks that resemble an actual ticket.

How is this different from an "AI-allowed" interview?

This is the distinction that actually matters, and it's easy to miss. An AI-allowed interview keeps the old format — a self-contained algorithm problem, a rubric built for hand-written code — and simply doesn't forbid opening an AI tool. It measures the same thing it always did, plus noise from whether a given candidate happened to lean on AI that day. An AI-native interview redesigns the task and the rubric around agent use: the problem is scoped so an agent alone can't trivially solve it, and the scoring explicitly covers direction, verification, and override judgment. "Allowed" is a permission. "Native" is a design choice.

Who's actually running AI-native interviews right now?

This isn't a hypothetical format. Meta began piloting an AI-enabled coding round in its software engineering onsite loop in October 2025, built on a specialized environment with an AI assistant included, and has been expanding it through 2026 — candidates at senior levels can have their entire coding round be AI-enabled rather than AI-free (reporting on the rollout from interviewing.io). CodeSignal has shipped a dedicated agentic interviewing product built around the same agents candidates use on the job. The direction is consistent across both: stop testing whether someone can avoid AI, and start testing whether they can direct it.

What does an AI-native interview actually evaluate?

• Problem framing. Can the candidate turn an ambiguous task into a scope an agent can act on, instead of either over-specifying or just handing the whole ticket to the agent unexamined.
• Verification.Do they check an agent's output before treating it as done — read the diff, run it, question an assumption it made — or accept whatever looks plausible.
• Override judgment. When the agent is confidently wrong, does the candidate catch it and redirect, or let a bad suggestion ship.
• Ownership. Can they explain what was built well enough to defend a specific decision in it, regardless of whether they or the agent typed it.

Where DeftBench fits

DeftBenchis built specifically for this format: a real browser-based agent workspace where a candidate works an ambiguous problem with a coding agent of their choice, and the full session — prompts, tool calls, diffs — is captured as telemetry with human and agent actions attributed separately. That turns an AI-native interview from a format into evidence: each score in the evaluation points back to a specific moment in the session instead of an interviewer's memory of it. For the related question of what to test for once a team moves to this format, see how to interview engineers who use AI coding agents, or compare the approach directly against an established platform's AI features in the DeftBench vs CoderPad comparison. If you're thinking about moving your own loop in this direction, talk to us about a pilot.